matlab_project

一些MATLAB的有关无线信道衰落的仿真源代码(Some of the wireless channel fading MATLAB simulation source code)

Custom tools for MATLAB supporting analysis and design of nonlinear control systems are introduced in this paper. The paper demonstrates their use to solve sample nonlinear control problems and presents some of the MATLAB algorithms involved

matlab在控制系统中应用的书籍，详细介绍了在各个环节中的应用(control system in Matlab Application of books, described in detail all the aspects of the application)

使用MATLAB程序编写的MP4视频转换程序，支持iBEwcP格式和viLAedU格式的转换，解码时音视频数据的获取就是建立在此之上的,学习MP4的最佳程序 (Using MATLAB programming MP4 video conversion program that supportsiBEwcP format and formatviLAedU conversion, access is built on top of this, the best learning program MP4 audio and video data decoding )

Floyd 算法, 程序, 代码, 采用Matlab实现(Floyd algorithm, program, code, using Matlab Implementation)

说明：  this is new approach for fuel cell control.it is really helpfull.

实现了SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks算法，适合做传感器网络分簇算法的人研究。(SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks , we implement the algorithm, and this code is suitable for those who do research in cluster algorithm in Wireless sensor network )

说明：  三维模式时域有限元求解电磁场，计算电磁学初学者，编写的程序代码，希望可以帮助广大网友(Three-dimensional time-domain finite element method solves electromagnetic field, calculates electromagnetic beginners, compiles the program code, hoping to help the vast number of netizens.)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

用matlab实现数据采集的方法 (Using matlab to achieve data acquisition methods)